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#!/usr/bin/python
###############################################################
# Copyright (c) 2017 ZTE Corporation
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
##############################################################################
import json
import numbers
from ansible.plugins.action import ActionBase
from ansible.utils.display import Display
from asq.initiators import query
import humanfriendly
from numpy import mean
import yaml
from qtip.util.export_to import export_to_file
display = Display()
class ActionModule(ActionBase):
def run(self, tmp=None, task_vars=None):
if task_vars is None:
task_vars = dict()
result = super(ActionModule, self).run(tmp, task_vars)
if result.get('skipped', False):
return result
with open(self._task.args.get('spec')) as stream:
spec = yaml.safe_load(stream)
metrics_files = self._task.args.get('metrics')
metrics = {}
for metric, filename in metrics_files.items():
with open(filename) as f:
metrics[metric] = json.load(f)
with open(self._task.args.get('sysinfo')) as f:
data = json.load(f)
sysinfo = dict([(k['name'], data[k['name']][0]) for k in spec['system_info']])
dest = self._task.args.get('dest')
baseline_file = self._task.args.get('baseline')
if baseline_file is not None:
with open(baseline_file) as f:
baseline = json.load(f)
return calc_qpi(spec, metrics, sysinfo, baseline, dest=dest)
else:
return calc_qpi(spec, metrics, sysinfo, None, dest=dest)
@export_to_file
def calc_qpi(qpi_spec, metrics, sysinfo, qpi_baseline):
display.vv("calculate QPI {}".format(qpi_spec['name']))
display.vvv("spec: {}".format(qpi_spec))
display.vvv("metrics: {}".format(metrics))
display.vvv("baseline: {}".format(qpi_baseline))
section_results = []
qpi_score = 0
if qpi_baseline:
for s in qpi_spec['sections']:
s_baseline = query(qpi_baseline['sections']).first(
lambda section: section['name'] == s['name'])
section_results.append(calc_section(s, metrics, s_baseline))
# TODO(yujunz): use formula in spec
qpi_score = int(
mean([r['score'] for r in section_results]) * qpi_baseline['score'])
else:
for s in qpi_spec['sections']:
section_results.append(calc_section(s, metrics))
results = {
'score': qpi_score,
'name': qpi_spec['name'],
'description': qpi_spec['description'],
'system_info': sysinfo,
'sections': section_results,
'spec': "https://git.opnfv.org/qtip/tree/resources/QPI/compute.yaml",
'baseline': "https://git.opnfv.org/qtip/tree/resources/baselines/compute.json"
}
return results
def calc_section(section_spec, metrics, section_baseline=None):
display.vv("calculate section {}".format(section_spec['name']))
display.vvv("spec: {}".format(section_spec))
display.vvv("metrics: {}".format(metrics))
display.vvv("baseline: {}".format(section_baseline))
metric_results = []
section_score = 0
if section_baseline:
for m in section_spec['metrics']:
m_baseline = query(section_baseline['metrics']).first(
lambda metric: metric['name'] == m['name'])
metric_results.append(calc_metric(m, metrics[m['name']], m_baseline))
section_score = mean([r['score'] for r in metric_results])
else:
for m in section_spec['metrics']:
metric_results.append(calc_metric(m, metrics[m['name']]))
# TODO(yujunz): use formula in spec
return {
'score': section_score,
'name': section_spec['name'],
'description': section_spec.get('description', 'section'),
'metrics': metric_results
}
def calc_metric(metric_spec, metrics, metric_baseline=None):
display.vv("calculate metric {}".format(metric_spec['name']))
display.vvv("spec: {}".format(metric_spec))
display.vvv("metrics: {}".format(metrics))
display.vvv("baseline: {}".format(metric_baseline))
# TODO(yujunz): use formula in spec
workload_results = []
metric_score = 0
if metric_baseline:
for w in metric_spec['workloads']:
w_baseline = query(metric_baseline['workloads']).first(
lambda workload: workload['name'] == w['name'])
workload_results.append({
'name': w['name'],
'description': 'workload',
'score': calc_score(metrics[w['name']], w_baseline['baseline']),
'result': metrics[w['name']][0]
})
metric_score = mean([r['score'] for r in workload_results])
else:
for w in metric_spec['workloads']:
workload_results.append({
'name': w['name'],
'baseline': metrics[w['name']][0]
})
return {
'score': metric_score,
'name': metric_spec['name'],
'description': metric_spec.get('description', 'metric'),
'workloads': workload_results
}
def calc_score(metrics, baseline):
if not isinstance(baseline, numbers.Number):
baseline = humanfriendly.parse_size(baseline)
return mean(
[m if isinstance(m, numbers.Number) else humanfriendly.parse_size(m)
for m in metrics]) / baseline
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